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Mining and Control of Network Traffic by Computational Intelligence pp 87–145Cite as

Predictive Models of Network Traffic Load

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 342))

Abstract

Understanding the dynamics and performance of packet switched networks on the basis of measurements enables practitioners to optimize resources. As network measurement research further advances and new measurement tools and infrastructures are available, the task of network operation becomes more and more complex. In this chapter we apply the methodology developed in the previous chapter to time series concerning network traffic load. An extensive predictability analysis is performed using the same nonparametric residual variance estimation technique that is integrated into the prediction methodology. Based on the predictability results, fuzzy inference based models that are both interpretable and accurate are derived for a wide set of heterogeneous time series for network traffic.

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Authors
  1. Federico Montesino Pouzols
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  2. Diego R. Lopez
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  3. Angel Barriga Barros
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Pouzols, F.M., Lopez, D.R., Barros, A.B. (2011). Predictive Models of Network Traffic Load. In: Mining and Control of Network Traffic by Computational Intelligence. Studies in Computational Intelligence, vol 342. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18084-2_3

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  • DOI: https://doi.org/10.1007/978-3-642-18084-2_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-18083-5

  • Online ISBN: 978-3-642-18084-2

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